Overview

Dataset statistics

Number of variables33
Number of observations22856
Missing cells59540
Missing cells (%)7.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory5.8 MiB
Average record size in memory264.0 B

Variable types

Categorical24
Numeric9

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
사용유형명 has a high cardinality: 101 distinct values High cardinality
예약자평균값 has a high cardinality: 116 distinct values High cardinality
예약자사용성향값 has a high cardinality: 236 distinct values High cardinality
예약년월일 has a high cardinality: 592 distinct values High cardinality
사용년월일 has a high cardinality: 633 distinct values High cardinality
사용일자 is highly correlated with 비거주자금액 and 1 other fieldsHigh correlation
거주자금액 is highly correlated with 비거주자금액High correlation
비거주자금액 is highly correlated with 사용일자 and 2 other fieldsHigh correlation
거주자인원수 is highly correlated with 비거주자인원수High correlation
비거주자인원수 is highly correlated with 거주자인원수High correlation
예약일자 is highly correlated with 사용일자 and 1 other fieldsHigh correlation
예약시간 is highly correlated with 예약시간(시)High correlation
예약시간(시) is highly correlated with 예약시간High correlation
사용일자 is highly correlated with 비거주자금액 and 1 other fieldsHigh correlation
거주자금액 is highly correlated with 비거주자금액High correlation
비거주자금액 is highly correlated with 사용일자 and 2 other fieldsHigh correlation
거주자인원수 is highly correlated with 비거주자인원수High correlation
비거주자인원수 is highly correlated with 거주자인원수High correlation
예약일자 is highly correlated with 사용일자 and 1 other fieldsHigh correlation
예약시간 is highly correlated with 예약시간(시)High correlation
예약시간(시) is highly correlated with 예약시간High correlation
사용일자 is highly correlated with 비거주자금액 and 1 other fieldsHigh correlation
거주자금액 is highly correlated with 비거주자금액High correlation
비거주자금액 is highly correlated with 사용일자 and 2 other fieldsHigh correlation
거주자인원수 is highly correlated with 비거주자인원수High correlation
비거주자인원수 is highly correlated with 거주자인원수High correlation
예약일자 is highly correlated with 사용일자 and 1 other fieldsHigh correlation
예약시간 is highly correlated with 예약시간(시)High correlation
예약시간(시) is highly correlated with 예약시간High correlation
거주자인원수 is highly correlated with 비거주자인원수High correlation
골프장멤버십값 is highly correlated with 골프장지역명 and 3 other fieldsHigh correlation
결제수단명 is highly correlated with 골프장지역명 and 2 other fieldsHigh correlation
골프장지역명 is highly correlated with 골프장멤버십값 and 4 other fieldsHigh correlation
비거주자인원수 is highly correlated with 거주자인원수High correlation
골프장유형값 is highly correlated with 골프장멤버십값 and 3 other fieldsHigh correlation
골프장명 is highly correlated with 골프장멤버십값 and 4 other fieldsHigh correlation
비거주자나눔 is highly correlated with 해당년도High correlation
판매업체명 is highly correlated with 골프장멤버십값 and 4 other fieldsHigh correlation
해당년도 is highly correlated with 비거주자나눔High correlation
골프장명 is highly correlated with 골프장지역명 and 11 other fieldsHigh correlation
골프장지역명 is highly correlated with 골프장명 and 5 other fieldsHigh correlation
골프장멤버십값 is highly correlated with 골프장명 and 5 other fieldsHigh correlation
골프장유형값 is highly correlated with 골프장명 and 5 other fieldsHigh correlation
사용일자 is highly correlated with 골프장명 and 5 other fieldsHigh correlation
사용시간값 is highly correlated with 골프장명 and 4 other fieldsHigh correlation
거주자금액 is highly correlated with 비거주자금액 and 1 other fieldsHigh correlation
비거주자금액 is highly correlated with 골프장명 and 6 other fieldsHigh correlation
결제수단명 is highly correlated with 골프장명 and 2 other fieldsHigh correlation
거주자인원수 is highly correlated with 비거주자인원수High correlation
비거주자인원수 is highly correlated with 거주자인원수High correlation
예약일자 is highly correlated with 골프장명 and 5 other fieldsHigh correlation
예약시간 is highly correlated with 예약시간(시) and 1 other fieldsHigh correlation
판매업체명 is highly correlated with 골프장명 and 10 other fieldsHigh correlation
해당년도 is highly correlated with 골프장명 and 4 other fieldsHigh correlation
거주자금액나눔 is highly correlated with 골프장명 and 6 other fieldsHigh correlation
비거주자나눔 is highly correlated with 골프장명 and 10 other fieldsHigh correlation
날짜차이 is highly correlated with 날짜차이요일High correlation
날짜차이요일 is highly correlated with 날짜차이High correlation
예약시간(시) is highly correlated with 예약시간 and 1 other fieldsHigh correlation
예약시간대별 is highly correlated with 예약시간 and 1 other fieldsHigh correlation
사용일통계 is highly correlated with 사용시간값High correlation
예약자연령대코드 has 7605 (33.3%) missing values Missing
예약자평균값 has 11299 (49.4%) missing values Missing
예약자골프경력값 has 13356 (58.4%) missing values Missing
예약자평균사용값 has 13358 (58.4%) missing values Missing
예약자사용성향값 has 13918 (60.9%) missing values Missing
비거주자금액 has 5799 (25.4%) zeros Zeros
예약시간(시) has 231 (1.0%) zeros Zeros

Reproduction

Analysis started2022-03-17 00:37:18.117752
Analysis finished2022-03-17 00:37:33.322579
Duration15.2 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

골프장명
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
라온GC
6814 
아덴힐GC
5221 
더클래식CC
3847 
해비치CC
3468 
타미우스CC
789 
Other values (14)
2717 

Length

Max length14
Median length5
Mean length5.237443122
Min length4

Characters and Unicode

Total characters119707
Distinct characters58
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아덴힐GC
2nd row라온GC
3rd row그린필드CC
4th row라온GC
5th row그린필드CC

Common Values

ValueCountFrequency (%)
라온GC6814
29.8%
아덴힐GC5221
22.8%
더클래식CC3847
16.8%
해비치CC3468
15.2%
타미우스CC789
 
3.5%
사이프러스CC561
 
2.5%
오라CC 527
 
2.3%
그린필드CC(구 제피로스)488
 
2.1%
롯데스카이힐제주CC401
 
1.8%
제주CC235
 
1.0%
Other values (9)505
 
2.2%

Length

2022-03-17T09:37:33.385364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
라온gc6814
29.2%
아덴힐gc5221
22.4%
더클래식cc3847
16.5%
해비치cc3468
14.9%
타미우스cc789
 
3.4%
사이프러스cc561
 
2.4%
오라cc527
 
2.3%
그린필드cc(구488
 
2.1%
제피로스488
 
2.1%
롯데스카이힐제주cc401
 
1.7%
Other values (10)740
 
3.2%

Most occurring characters

ValueCountFrequency (%)
C33662
28.1%
G12050
 
10.1%
7454
 
6.2%
6814
 
5.7%
5622
 
4.7%
5221
 
4.4%
5221
 
4.4%
3847
 
3.2%
3847
 
3.2%
3847
 
3.2%
Other values (48)32122
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter72004
60.2%
Uppercase Letter45712
38.2%
Space Separator1015
 
0.8%
Close Punctuation488
 
0.4%
Open Punctuation488
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7454
 
10.4%
6814
 
9.5%
5622
 
7.8%
5221
 
7.3%
5221
 
7.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3468
 
4.8%
Other values (43)22816
31.7%
Uppercase Letter
ValueCountFrequency (%)
C33662
73.6%
G12050
 
26.4%
Space Separator
ValueCountFrequency (%)
1015
100.0%
Close Punctuation
ValueCountFrequency (%)
)488
100.0%
Open Punctuation
ValueCountFrequency (%)
(488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul72004
60.2%
Latin45712
38.2%
Common1991
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7454
 
10.4%
6814
 
9.5%
5622
 
7.8%
5221
 
7.3%
5221
 
7.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3468
 
4.8%
Other values (43)22816
31.7%
Common
ValueCountFrequency (%)
1015
51.0%
)488
24.5%
(488
24.5%
Latin
ValueCountFrequency (%)
C33662
73.6%
G12050
 
26.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul72004
60.2%
ASCII47703
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C33662
70.6%
G12050
 
25.3%
1015
 
2.1%
)488
 
1.0%
(488
 
1.0%
Hangul
ValueCountFrequency (%)
7454
 
10.4%
6814
 
9.5%
5622
 
7.8%
5221
 
7.3%
5221
 
7.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3847
 
5.3%
3468
 
4.8%
Other values (43)22816
31.7%

골프장지역명
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
서부권
12960 
동부권
8155 
제주시권
 
1269
서귀포권
 
437
제주시내권
 
35

Length

Max length5
Median length3
Mean length3.077703885
Min length3

Characters and Unicode

Total characters70344
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서부권
2nd row서부권
3rd row제주시내권
4th row서부권
5th row제주시내권

Common Values

ValueCountFrequency (%)
서부권12960
56.7%
동부권8155
35.7%
제주시권1269
 
5.6%
서귀포권437
 
1.9%
제주시내권35
 
0.2%

Length

2022-03-17T09:37:33.497987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:33.560834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
서부권12960
56.7%
동부권8155
35.7%
제주시권1269
 
5.6%
서귀포권437
 
1.9%
제주시내권35
 
0.2%

Most occurring characters

ValueCountFrequency (%)
22856
32.5%
21115
30.0%
13397
19.0%
8155
 
11.6%
1304
 
1.9%
1304
 
1.9%
1304
 
1.9%
437
 
0.6%
437
 
0.6%
35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter70344
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22856
32.5%
21115
30.0%
13397
19.0%
8155
 
11.6%
1304
 
1.9%
1304
 
1.9%
1304
 
1.9%
437
 
0.6%
437
 
0.6%
35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul70344
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22856
32.5%
21115
30.0%
13397
19.0%
8155
 
11.6%
1304
 
1.9%
1304
 
1.9%
1304
 
1.9%
437
 
0.6%
437
 
0.6%
35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul70344
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22856
32.5%
21115
30.0%
13397
19.0%
8155
 
11.6%
1304
 
1.9%
1304
 
1.9%
1304
 
1.9%
437
 
0.6%
437
 
0.6%
35
 
< 0.1%

골프장멤버십값
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
대중제
16411 
회원제+대중제
5093 
회원제
 
1352

Length

Max length7
Median length3
Mean length3.891319566
Min length3

Characters and Unicode

Total characters88940
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대중제
2nd row대중제
3rd row회원제
4th row대중제
5th row회원제

Common Values

ValueCountFrequency (%)
대중제16411
71.8%
회원제+대중제5093
 
22.3%
회원제1352
 
5.9%

Length

2022-03-17T09:37:33.635628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:33.692459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
대중제16411
71.8%
회원제+대중제5093
 
22.3%
회원제1352
 
5.9%

Most occurring characters

ValueCountFrequency (%)
27949
31.4%
21504
24.2%
21504
24.2%
6445
 
7.2%
6445
 
7.2%
+5093
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter83847
94.3%
Math Symbol5093
 
5.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27949
33.3%
21504
25.6%
21504
25.6%
6445
 
7.7%
6445
 
7.7%
Math Symbol
ValueCountFrequency (%)
+5093
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul83847
94.3%
Common5093
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
27949
33.3%
21504
25.6%
21504
25.6%
6445
 
7.7%
6445
 
7.7%
Common
ValueCountFrequency (%)
+5093
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul83847
94.3%
ASCII5093
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
27949
33.3%
21504
25.6%
21504
25.6%
6445
 
7.7%
6445
 
7.7%
ASCII
ValueCountFrequency (%)
+5093
100.0%

골프장유형값
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
18
10028 
27
7710 
36
5118 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters45712
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18
2nd row27
3rd row18
4th row27
5th row18

Common Values

ValueCountFrequency (%)
1810028
43.9%
277710
33.7%
365118
22.4%

Length

2022-03-17T09:37:33.746339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:33.798089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1810028
43.9%
277710
33.7%
365118
22.4%

Most occurring characters

ValueCountFrequency (%)
110028
21.9%
810028
21.9%
27710
16.9%
77710
16.9%
35118
11.2%
65118
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45712
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110028
21.9%
810028
21.9%
27710
16.9%
77710
16.9%
35118
11.2%
65118
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common45712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110028
21.9%
810028
21.9%
27710
16.9%
77710
16.9%
35118
11.2%
65118
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII45712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110028
21.9%
810028
21.9%
27710
16.9%
77710
16.9%
35118
11.2%
65118
11.2%

사용일자
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct633
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20208089.74
Minimum20200322
Maximum20220413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:33.871953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20200322
5-th percentile20200629
Q120201119
median20210602
Q320210827
95-th percentile20211027
Maximum20220413
Range20091
Interquartile range (IQR)9708

Descriptive statistics

Standard deviation5034.77992
Coefficient of variation (CV)0.0002491467518
Kurtosis-0.4641956382
Mean20208089.74
Median Absolute Deviation (MAD)322
Skewness-0.2875018869
Sum4.618760992 × 1011
Variance25349008.84
MonotonicityNot monotonic
2022-03-17T09:37:34.118210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210816198
 
0.9%
20210922146
 
0.6%
20210926144
 
0.6%
20210630138
 
0.6%
20210924137
 
0.6%
20210730132
 
0.6%
20210925130
 
0.6%
20211011127
 
0.6%
20210923121
 
0.5%
20210927118
 
0.5%
Other values (623)21465
93.9%
ValueCountFrequency (%)
202003221
 
< 0.1%
202003271
 
< 0.1%
202003283
< 0.1%
202003294
< 0.1%
202003301
 
< 0.1%
202004033
< 0.1%
202004043
< 0.1%
202004061
 
< 0.1%
202004071
 
< 0.1%
202004092
< 0.1%
ValueCountFrequency (%)
202204131
 
< 0.1%
202204081
 
< 0.1%
202204073
 
< 0.1%
202204065
 
< 0.1%
202204052
 
< 0.1%
202204041
 
< 0.1%
202204014
 
< 0.1%
2022033111
< 0.1%
2022033011
< 0.1%
2022032917
0.1%

사용시간값
Real number (ℝ≥0)

HIGH CORRELATION

Distinct412
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987.4712111
Minimum101
Maximum1710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:34.223859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile659
Q1735
median843
Q31236
95-th percentile1406
Maximum1710
Range1609
Interquartile range (IQR)501

Descriptive statistics

Standard deviation276.7766113
Coefficient of variation (CV)0.280288284
Kurtosis-1.198427941
Mean987.4712111
Median Absolute Deviation (MAD)143
Skewness0.4402272353
Sum22569642
Variance76605.29254
MonotonicityNot monotonic
2022-03-17T09:37:34.322608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705361
 
1.6%
858321
 
1.4%
719318
 
1.4%
713286
 
1.3%
851282
 
1.2%
844278
 
1.2%
706268
 
1.2%
726267
 
1.2%
748259
 
1.1%
724234
 
1.0%
Other values (402)19982
87.4%
ValueCountFrequency (%)
1011
 
< 0.1%
5521
 
< 0.1%
5583
 
< 0.1%
6053
 
< 0.1%
6102
 
< 0.1%
6113
 
< 0.1%
6167
 
< 0.1%
6173
 
< 0.1%
6183
 
< 0.1%
62024
0.1%
ValueCountFrequency (%)
17101
 
< 0.1%
17032
 
< 0.1%
16572
 
< 0.1%
16502
 
< 0.1%
16433
 
< 0.1%
163636
 
0.2%
1629132
0.6%
1622105
0.5%
1615119
0.5%
1608124
0.5%

사용유형명
Categorical

HIGH CARDINALITY

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
LAKE
4111 
새별
2626 
왕이메
2595 
PINE
2392 
STONE
2013 
Other values (96)
9119 

Length

Max length21
Median length4
Mean length4.455372769
Min length1

Characters and Unicode

Total characters101832
Distinct characters103
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.1%

Sample

1st row새별
2nd rowLAKE
3rd rowM
4th rowLAKE
5th rowM

Common Values

ValueCountFrequency (%)
LAKE4111
18.0%
새별2626
11.5%
왕이메2595
11.4%
PINE2392
10.5%
STONE2013
8.8%
대중제(Valley)1957
8.6%
대중제1142
 
5.0%
회원제951
 
4.2%
VALLEY827
 
3.6%
SKY566
 
2.5%
Other values (91)3676
16.1%

Length

2022-03-17T09:37:34.425253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lake4111
17.9%
새별2626
11.5%
왕이메2595
11.3%
pine2392
10.4%
stone2013
8.8%
대중제(valley1963
8.6%
valley1181
 
5.2%
대중제1152
 
5.0%
회원제954
 
4.2%
sky566
 
2.5%
Other values (85)3358
14.7%

Most occurring characters

ValueCountFrequency (%)
E9805
 
9.6%
L6184
 
6.1%
A5510
 
5.4%
l5302
 
5.2%
4921
 
4.8%
K4717
 
4.6%
N4649
 
4.6%
3823
 
3.8%
3822
 
3.8%
V3485
 
3.4%
Other values (93)49614
48.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter51474
50.5%
Other Letter30061
29.5%
Lowercase Letter14516
 
14.3%
Close Punctuation2799
 
2.7%
Open Punctuation2799
 
2.7%
Space Separator92
 
0.1%
Math Symbol42
 
< 0.1%
Other Punctuation35
 
< 0.1%
Decimal Number9
 
< 0.1%
Connector Punctuation5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4921
16.4%
3823
12.7%
3822
12.7%
2626
8.7%
2626
8.7%
2595
8.6%
2595
8.6%
2595
8.6%
1100
 
3.7%
1100
 
3.7%
Other values (51)2258
7.5%
Uppercase Letter
ValueCountFrequency (%)
E9805
19.0%
L6184
12.0%
A5510
10.7%
K4717
9.2%
N4649
9.0%
V3485
 
6.8%
S3334
 
6.5%
P2746
 
5.3%
T2661
 
5.2%
O2514
 
4.9%
Other values (10)5869
11.4%
Lowercase Letter
ValueCountFrequency (%)
l5302
36.5%
a2686
18.5%
y2651
18.3%
e2649
18.2%
s319
 
2.2%
o301
 
2.1%
t301
 
2.1%
r299
 
2.1%
n4
 
< 0.1%
i2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
13
33.3%
03
33.3%
51
 
11.1%
61
 
11.1%
21
 
11.1%
Close Punctuation
ValueCountFrequency (%)
)2799
100.0%
Open Punctuation
ValueCountFrequency (%)
(2799
100.0%
Space Separator
ValueCountFrequency (%)
92
100.0%
Math Symbol
ValueCountFrequency (%)
+42
100.0%
Other Punctuation
ValueCountFrequency (%)
/35
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65990
64.8%
Hangul30061
29.5%
Common5781
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4921
16.4%
3823
12.7%
3822
12.7%
2626
8.7%
2626
8.7%
2595
8.6%
2595
8.6%
2595
8.6%
1100
 
3.7%
1100
 
3.7%
Other values (51)2258
7.5%
Latin
ValueCountFrequency (%)
E9805
14.9%
L6184
 
9.4%
A5510
 
8.3%
l5302
 
8.0%
K4717
 
7.1%
N4649
 
7.0%
V3485
 
5.3%
S3334
 
5.1%
P2746
 
4.2%
a2686
 
4.1%
Other values (21)17572
26.6%
Common
ValueCountFrequency (%)
)2799
48.4%
(2799
48.4%
92
 
1.6%
+42
 
0.7%
/35
 
0.6%
_5
 
0.1%
13
 
0.1%
03
 
0.1%
51
 
< 0.1%
61
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII71771
70.5%
Hangul30061
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E9805
13.7%
L6184
 
8.6%
A5510
 
7.7%
l5302
 
7.4%
K4717
 
6.6%
N4649
 
6.5%
V3485
 
4.9%
S3334
 
4.6%
)2799
 
3.9%
(2799
 
3.9%
Other values (32)23187
32.3%
Hangul
ValueCountFrequency (%)
4921
16.4%
3823
12.7%
3822
12.7%
2626
8.7%
2626
8.7%
2595
8.6%
2595
8.6%
2595
8.6%
1100
 
3.7%
1100
 
3.7%
Other values (51)2258
7.5%

거주자금액
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct161
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115614.4228
Minimum50000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:34.520907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile65000
Q196000
median110000
Q3138500
95-th percentile170000
Maximum1000000
Range950000
Interquartile range (IQR)42500

Descriptive statistics

Standard deviation34257.77192
Coefficient of variation (CV)0.2963105388
Kurtosis96.16511418
Mean115614.4228
Median Absolute Deviation (MAD)20000
Skewness3.997222609
Sum2642483248
Variance1173594937
MonotonicityNot monotonic
2022-03-17T09:37:34.617627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700001630
 
7.1%
1075001359
 
5.9%
1000001163
 
5.1%
1200001047
 
4.6%
1100001006
 
4.4%
105000873
 
3.8%
90000816
 
3.6%
130000809
 
3.5%
160000593
 
2.6%
99500593
 
2.6%
Other values (151)12967
56.7%
ValueCountFrequency (%)
5000055
 
0.2%
5500070
 
0.3%
575001
 
< 0.1%
60000582
 
2.5%
65000436
 
1.9%
6550052
 
0.2%
6600024
 
0.1%
675006
 
< 0.1%
700001630
7.1%
705003
 
< 0.1%
ValueCountFrequency (%)
10000003
 
< 0.1%
9999992
 
< 0.1%
3000001
 
< 0.1%
2225005
 
< 0.1%
21250014
 
0.1%
21000014
 
0.1%
2045002
 
< 0.1%
20300022
 
0.1%
20250060
0.3%
2020001
 
< 0.1%

비거주자금액
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct114
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125782.0487
Minimum0
Maximum1450002
Zeros5799
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:34.715213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median154500
Q3182500
95-th percentile222500
Maximum1450002
Range1450002
Interquartile range (IQR)182500

Descriptive statistics

Standard deviation83458.81492
Coefficient of variation (CV)0.6635192845
Kurtosis9.619621337
Mean125782.0487
Median Absolute Deviation (MAD)45500
Skewness0.2354926976
Sum2874874504
Variance6965373788
MonotonicityNot monotonic
2022-03-17T09:37:34.814879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05799
25.4%
1825003140
13.7%
2225001519
 
6.6%
900001037
 
4.5%
160000916
 
4.0%
202500742
 
3.2%
200000728
 
3.2%
190000659
 
2.9%
150000653
 
2.9%
175000642
 
2.8%
Other values (104)7021
30.7%
ValueCountFrequency (%)
05799
25.4%
80000412
 
1.8%
900001037
 
4.5%
950003
 
< 0.1%
100000364
 
1.6%
1010003
 
< 0.1%
10500010
 
< 0.1%
10600018
 
0.1%
1090002
 
< 0.1%
110000186
 
0.8%
ValueCountFrequency (%)
14500023
 
< 0.1%
10000003
 
< 0.1%
9999992
 
< 0.1%
26700014
 
0.1%
260000150
 
0.7%
2505003
 
< 0.1%
2350009
 
< 0.1%
230000472
 
2.1%
2270008
 
< 0.1%
2225001519
6.6%

결제수단명
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
현장결제
21362 
선결제
 
1494

Length

Max length4
Median length4
Mean length3.934634232
Min length3

Characters and Unicode

Total characters89930
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row현장결제
2nd row현장결제
3rd row선결제
4th row현장결제
5th row선결제

Common Values

ValueCountFrequency (%)
현장결제21362
93.5%
선결제1494
 
6.5%

Length

2022-03-17T09:37:34.910641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:34.962491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
현장결제21362
93.5%
선결제1494
 
6.5%

Most occurring characters

ValueCountFrequency (%)
22856
25.4%
22856
25.4%
21362
23.8%
21362
23.8%
1494
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter89930
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22856
25.4%
22856
25.4%
21362
23.8%
21362
23.8%
1494
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul89930
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22856
25.4%
22856
25.4%
21362
23.8%
21362
23.8%
1494
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul89930
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22856
25.4%
22856
25.4%
21362
23.8%
21362
23.8%
1494
 
1.7%

총인원수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
4
22535 
3
 
305
2
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22856
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

Length

2022-03-17T09:37:35.029248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:35.077279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

Most occurring characters

ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common22856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII22856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
422535
98.6%
3305
 
1.3%
216
 
0.1%

거주자인원수
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
4
20640 
0
 
676
3
 
586
2
 
560
1
 
394

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22856
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

Length

2022-03-17T09:37:35.128114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:35.176947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

Most occurring characters

ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common22856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII22856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
420640
90.3%
0676
 
3.0%
3586
 
2.6%
2560
 
2.5%
1394
 
1.7%

비거주자인원수
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
0
20879 
4
 
637
2
 
569
3
 
399
1
 
372

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22856
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

Length

2022-03-17T09:37:35.238740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:35.293473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

Most occurring characters

ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common22856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII22856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020879
91.4%
4637
 
2.8%
2569
 
2.5%
3399
 
1.7%
1372
 
1.6%

예약일자
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct592
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20207971.85
Minimum20200321
Maximum20220310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:35.370302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20200321
5-th percentile20200621.75
Q120201015
median20210524
Q320210815
95-th percentile20211014
Maximum20220310
Range19989
Interquartile range (IQR)9800

Descriptive statistics

Standard deviation5078.018793
Coefficient of variation (CV)0.0002512878993
Kurtosis-0.5241791686
Mean20207971.85
Median Absolute Deviation (MAD)384
Skewness-0.2537067006
Sum4.618734045 × 1011
Variance25786274.87
MonotonicityNot monotonic
2022-03-17T09:37:35.475933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210721172
 
0.8%
20210924155
 
0.7%
20210923146
 
0.6%
20210831140
 
0.6%
20210917136
 
0.6%
20210914133
 
0.6%
20210902126
 
0.6%
20210406124
 
0.5%
20210903124
 
0.5%
20210802123
 
0.5%
Other values (582)21477
94.0%
ValueCountFrequency (%)
202003211
 
< 0.1%
202003233
< 0.1%
202003252
 
< 0.1%
202003265
< 0.1%
202003272
 
< 0.1%
202003281
 
< 0.1%
202003292
 
< 0.1%
202003301
 
< 0.1%
202004012
 
< 0.1%
202004021
 
< 0.1%
ValueCountFrequency (%)
2022031089
0.4%
2022030959
0.3%
20220308103
0.5%
20220307109
0.5%
2022030640
 
0.2%
2022030557
0.2%
2022030477
0.3%
2022030378
0.3%
2022030296
0.4%
2022030192
0.4%

예약시간
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18720
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145856.9888
Minimum5
Maximum235946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:35.579586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile80240.5
Q1113488.25
median144927
Q3180416
95-th percentile215842.25
Maximum235946
Range235941
Interquartile range (IQR)66927.75

Descriptive statistics

Standard deviation45159.49121
Coefficient of variation (CV)0.3096148602
Kurtosis0.2234130454
Mean145856.9888
Median Absolute Deviation (MAD)31883.5
Skewness-0.3530211774
Sum3333707337
Variance2039379647
MonotonicityNot monotonic
2022-03-17T09:37:35.675277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
941125
 
< 0.1%
1810265
 
< 0.1%
2013205
 
< 0.1%
1557215
 
< 0.1%
949455
 
< 0.1%
1115485
 
< 0.1%
1045385
 
< 0.1%
1033585
 
< 0.1%
1050205
 
< 0.1%
1052165
 
< 0.1%
Other values (18710)22806
99.8%
ValueCountFrequency (%)
52
< 0.1%
232
< 0.1%
251
< 0.1%
282
< 0.1%
451
< 0.1%
1041
< 0.1%
1111
< 0.1%
1161
< 0.1%
1381
< 0.1%
1391
< 0.1%
ValueCountFrequency (%)
2359461
< 0.1%
2359251
< 0.1%
2359141
< 0.1%
2358461
< 0.1%
2358331
< 0.1%
2358222
< 0.1%
2358141
< 0.1%
2357541
< 0.1%
2357531
< 0.1%
2357481
< 0.1%

판매업체명
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
라온GC
6814 
아덴힐GC
5221 
섬프로
4504 
해비치CC
3379 
(주)부민가자투어
697 
Other values (8)
2241 

Length

Max length13
Median length5
Mean length4.764656983
Min length3

Characters and Unicode

Total characters108901
Distinct characters45
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아덴힐GC
2nd row라온GC
3rd row(주)부민가자투어
4th row라온GC
5th row(주)부민가자투어

Common Values

ValueCountFrequency (%)
라온GC6814
29.8%
아덴힐GC5221
22.8%
섬프로4504
19.7%
해비치CC3379
14.8%
(주)부민가자투어697
 
3.0%
사이프러스CC561
 
2.5%
슬기로운골프생활528
 
2.3%
섬프로(롯데스카이힐)399
 
1.7%
섬프로(타미우스)362
 
1.6%
엘리시안C.C136
 
0.6%
Other values (3)255
 
1.1%

Length

2022-03-17T09:37:35.764897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
라온gc6814
29.8%
아덴힐gc5221
22.8%
섬프로4504
19.7%
해비치cc3379
14.8%
주)부민가자투어697
 
3.0%
사이프러스cc561
 
2.5%
슬기로운골프생활528
 
2.3%
섬프로(롯데스카이힐399
 
1.7%
섬프로(타미우스362
 
1.6%
엘리시안c.c136
 
0.6%
Other values (3)255
 
1.1%

Most occurring characters

ValueCountFrequency (%)
C20377
18.7%
G12035
11.1%
6980
 
6.4%
6814
 
6.3%
6443
 
5.9%
5882
 
5.4%
5620
 
5.2%
5354
 
4.9%
5221
 
4.8%
5221
 
4.8%
Other values (35)28954
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter72880
66.9%
Uppercase Letter32412
29.8%
Open Punctuation1689
 
1.6%
Close Punctuation1689
 
1.6%
Other Punctuation136
 
0.1%
Space Separator95
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6980
 
9.6%
6814
 
9.3%
6443
 
8.8%
5882
 
8.1%
5620
 
7.7%
5354
 
7.3%
5221
 
7.2%
5221
 
7.2%
3468
 
4.8%
3468
 
4.8%
Other values (29)18409
25.3%
Uppercase Letter
ValueCountFrequency (%)
C20377
62.9%
G12035
37.1%
Open Punctuation
ValueCountFrequency (%)
(1689
100.0%
Close Punctuation
ValueCountFrequency (%)
)1689
100.0%
Other Punctuation
ValueCountFrequency (%)
.136
100.0%
Space Separator
ValueCountFrequency (%)
95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul72880
66.9%
Latin32412
29.8%
Common3609
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6980
 
9.6%
6814
 
9.3%
6443
 
8.8%
5882
 
8.1%
5620
 
7.7%
5354
 
7.3%
5221
 
7.2%
5221
 
7.2%
3468
 
4.8%
3468
 
4.8%
Other values (29)18409
25.3%
Common
ValueCountFrequency (%)
(1689
46.8%
)1689
46.8%
.136
 
3.8%
95
 
2.6%
Latin
ValueCountFrequency (%)
C20377
62.9%
G12035
37.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul72880
66.9%
ASCII36021
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C20377
56.6%
G12035
33.4%
(1689
 
4.7%
)1689
 
4.7%
.136
 
0.4%
95
 
0.3%
Hangul
ValueCountFrequency (%)
6980
 
9.6%
6814
 
9.3%
6443
 
8.8%
5882
 
8.1%
5620
 
7.7%
5354
 
7.3%
5221
 
7.2%
5221
 
7.2%
3468
 
4.8%
3468
 
4.8%
Other values (29)18409
25.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
1
11647 
0
6470 
2
4739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22856
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

Length

2022-03-17T09:37:35.835729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:35.882504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

Most occurring characters

ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common22856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII22856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111647
51.0%
06470
28.3%
24739
20.7%

예약자연령대코드
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing7605
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean41.08976461
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:36.058983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q140
median40
Q350
95-th percentile50
Maximum70
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.056717439
Coefficient of variation (CV)0.1960760183
Kurtosis0.3442558415
Mean41.08976461
Median Absolute Deviation (MAD)0
Skewness-0.1562504474
Sum626660
Variance64.91069589
MonotonicityNot monotonic
2022-03-17T09:37:36.122789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
407743
33.9%
504105
18.0%
302599
 
11.4%
60431
 
1.9%
20341
 
1.5%
1020
 
0.1%
7012
 
0.1%
(Missing)7605
33.3%
ValueCountFrequency (%)
1020
 
0.1%
20341
 
1.5%
302599
 
11.4%
407743
33.9%
504105
18.0%
60431
 
1.9%
7012
 
0.1%
ValueCountFrequency (%)
7012
 
0.1%
60431
 
1.9%
504105
18.0%
407743
33.9%
302599
 
11.4%
20341
 
1.5%
1020
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
도민
22034 
비도민
 
822

Length

Max length3
Median length2
Mean length2.035964298
Min length2

Characters and Unicode

Total characters46534
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도민
2nd row도민
3rd row도민
4th row도민
5th row도민

Common Values

ValueCountFrequency (%)
도민22034
96.4%
비도민822
 
3.6%

Length

2022-03-17T09:37:36.199578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:36.250365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
도민22034
96.4%
비도민822
 
3.6%

Most occurring characters

ValueCountFrequency (%)
22856
49.1%
22856
49.1%
822
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter46534
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22856
49.1%
22856
49.1%
822
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul46534
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22856
49.1%
22856
49.1%
822
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul46534
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22856
49.1%
22856
49.1%
822
 
1.8%

예약자평균값
Categorical

HIGH CARDINALITY
MISSING

Distinct116
Distinct (%)1.0%
Missing11299
Missing (%)49.4%
Memory size178.7 KiB
20
1892 
18
1267 
15
1095 
25
887 
10
861 
Other values (111)
5555 

Length

Max length11
Median length2
Mean length2.064895734
Min length1

Characters and Unicode

Total characters23864
Distinct characters41
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st row12
2nd row20
3rd row20
4th row14
5th row25

Common Values

ValueCountFrequency (%)
201892
 
8.3%
181267
 
5.5%
151095
 
4.8%
25887
 
3.9%
10861
 
3.8%
30804
 
3.5%
100710
 
3.1%
90430
 
1.9%
95331
 
1.4%
12306
 
1.3%
Other values (106)2974
 
13.0%
(Missing)11299
49.4%

Length

2022-03-17T09:37:36.304265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201897
16.4%
181270
11.0%
151101
 
9.5%
25889
 
7.7%
10861
 
7.4%
30805
 
7.0%
100710
 
6.1%
90430
 
3.7%
95331
 
2.9%
12306
 
2.6%
Other values (94)2958
25.6%

Most occurring characters

ValueCountFrequency (%)
05971
25.0%
15405
22.6%
23996
16.7%
52624
11.0%
81954
 
8.2%
31572
 
6.6%
91307
 
5.5%
6336
 
1.4%
4335
 
1.4%
7222
 
0.9%
Other values (31)142
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23722
99.4%
Other Letter104
 
0.4%
Math Symbol27
 
0.1%
Other Punctuation6
 
< 0.1%
Lowercase Letter4
 
< 0.1%
Space Separator1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
11.5%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
6
 
5.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
Other values (13)28
26.9%
Decimal Number
ValueCountFrequency (%)
05971
25.2%
15405
22.8%
23996
16.8%
52624
11.1%
81954
 
8.2%
31572
 
6.6%
91307
 
5.5%
6336
 
1.4%
4335
 
1.4%
7222
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
t1
25.0%
f1
25.0%
e1
25.0%
l1
25.0%
Other Punctuation
ValueCountFrequency (%)
*5
83.3%
#1
 
16.7%
Math Symbol
ValueCountFrequency (%)
+27
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common23756
99.5%
Hangul104
 
0.4%
Latin4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
11.5%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
6
 
5.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
Other values (13)28
26.9%
Common
ValueCountFrequency (%)
05971
25.1%
15405
22.8%
23996
16.8%
52624
11.0%
81954
 
8.2%
31572
 
6.6%
91307
 
5.5%
6336
 
1.4%
4335
 
1.4%
7222
 
0.9%
Other values (4)34
 
0.1%
Latin
ValueCountFrequency (%)
t1
25.0%
f1
25.0%
e1
25.0%
l1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23760
99.6%
Hangul104
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05971
25.1%
15405
22.7%
23996
16.8%
52624
11.0%
81954
 
8.2%
31572
 
6.6%
91307
 
5.5%
6336
 
1.4%
4335
 
1.4%
7222
 
0.9%
Other values (8)38
 
0.2%
Hangul
ValueCountFrequency (%)
12
11.5%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
9
 
8.7%
6
 
5.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
Other values (13)28
26.9%

예약자골프경력값
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing13356
Missing (%)58.4%
Memory size178.7 KiB
1~3년
3295 
4~6년
2758 
10년이상
2296 
1년미만
1151 

Length

Max length5
Median length4
Mean length4.241684211
Min length4

Characters and Unicode

Total characters40296
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10년이상
2nd row10년이상
3rd row10년이상
4th row10년이상
5th row1년미만

Common Values

ValueCountFrequency (%)
1~3년3295
 
14.4%
4~6년2758
 
12.1%
10년이상2296
 
10.0%
1년미만1151
 
5.0%
(Missing)13356
58.4%

Length

2022-03-17T09:37:36.377937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:36.431757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1~3년3295
34.7%
4~6년2758
29.0%
10년이상2296
24.2%
1년미만1151
 
12.1%

Most occurring characters

ValueCountFrequency (%)
9500
23.6%
16742
16.7%
~6053
15.0%
33295
 
8.2%
42758
 
6.8%
62758
 
6.8%
02296
 
5.7%
2296
 
5.7%
2296
 
5.7%
1151
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17849
44.3%
Other Letter16394
40.7%
Math Symbol6053
 
15.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9500
57.9%
2296
 
14.0%
2296
 
14.0%
1151
 
7.0%
1151
 
7.0%
Decimal Number
ValueCountFrequency (%)
16742
37.8%
33295
18.5%
42758
15.5%
62758
15.5%
02296
 
12.9%
Math Symbol
ValueCountFrequency (%)
~6053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common23902
59.3%
Hangul16394
40.7%

Most frequent character per script

Common
ValueCountFrequency (%)
16742
28.2%
~6053
25.3%
33295
13.8%
42758
11.5%
62758
11.5%
02296
 
9.6%
Hangul
ValueCountFrequency (%)
9500
57.9%
2296
 
14.0%
2296
 
14.0%
1151
 
7.0%
1151
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23902
59.3%
Hangul16394
40.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9500
57.9%
2296
 
14.0%
2296
 
14.0%
1151
 
7.0%
1151
 
7.0%
ASCII
ValueCountFrequency (%)
16742
28.2%
~6053
25.3%
33295
13.8%
42758
11.5%
62758
11.5%
02296
 
9.6%

예약자평균사용값
Categorical

MISSING

Distinct5
Distinct (%)0.1%
Missing13358
Missing (%)58.4%
Memory size178.7 KiB
2회
2618 
4회
2170 
5회이상
1823 
3회
1764 
1회이하
1123 

Length

Max length4
Median length2
Mean length2.620341124
Min length2

Characters and Unicode

Total characters24888
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5회이상
2nd row5회이상
3rd row3회
4th row4회
5th row3회

Common Values

ValueCountFrequency (%)
2회2618
 
11.5%
4회2170
 
9.5%
5회이상1823
 
8.0%
3회1764
 
7.7%
1회이하1123
 
4.9%
(Missing)13358
58.4%

Length

2022-03-17T09:37:36.532421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:36.592382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2회2618
27.6%
4회2170
22.8%
5회이상1823
19.2%
3회1764
18.6%
1회이하1123
11.8%

Most occurring characters

ValueCountFrequency (%)
9498
38.2%
2946
 
11.8%
22618
 
10.5%
42170
 
8.7%
51823
 
7.3%
1823
 
7.3%
31764
 
7.1%
11123
 
4.5%
1123
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter15390
61.8%
Decimal Number9498
38.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22618
27.6%
42170
22.8%
51823
19.2%
31764
18.6%
11123
11.8%
Other Letter
ValueCountFrequency (%)
9498
61.7%
2946
 
19.1%
1823
 
11.8%
1123
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul15390
61.8%
Common9498
38.2%

Most frequent character per script

Common
ValueCountFrequency (%)
22618
27.6%
42170
22.8%
51823
19.2%
31764
18.6%
11123
11.8%
Hangul
ValueCountFrequency (%)
9498
61.7%
2946
 
19.1%
1823
 
11.8%
1123
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul15390
61.8%
ASCII9498
38.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9498
61.7%
2946
 
19.1%
1823
 
11.8%
1123
 
7.3%
ASCII
ValueCountFrequency (%)
22618
27.6%
42170
22.8%
51823
19.2%
31764
18.6%
11123
11.8%

예약자사용성향값
Categorical

HIGH CARDINALITY
MISSING

Distinct236
Distinct (%)2.6%
Missing13918
Missing (%)60.9%
Memory size178.7 KiB
주중골프
1951 
골프초년생
1700 
주말골프
1479 
정기골프
 
291
주중골프,오후골프
 
270
Other values (231)
3247 

Length

Max length52
Median length4
Mean length6.685723876
Min length3

Characters and Unicode

Total characters59757
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.7%

Sample

1st row주말골프
2nd row주중골프,오후골프
3rd row주중골프,오후골프
4th row정기골프
5th row골프초년생

Common Values

ValueCountFrequency (%)
주중골프1951
 
8.5%
골프초년생1700
 
7.4%
주말골프1479
 
6.5%
정기골프291
 
1.3%
주중골프,오후골프270
 
1.2%
새벽골프255
 
1.1%
번개골프230
 
1.0%
오후골프189
 
0.8%
내기파165
 
0.7%
제주여행126
 
0.6%
Other values (226)2282
 
10.0%
(Missing)13918
60.9%

Length

2022-03-17T09:37:36.673096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주중골프1951
21.8%
골프초년생1700
19.0%
주말골프1479
16.5%
정기골프291
 
3.3%
주중골프,오후골프270
 
3.0%
새벽골프255
 
2.9%
번개골프230
 
2.6%
오후골프189
 
2.1%
내기파165
 
1.8%
제주여행126
 
1.4%
Other values (226)2282
25.5%

Most occurring characters

ValueCountFrequency (%)
11750
19.7%
11750
19.7%
6387
10.7%
,4537
 
7.6%
3552
 
5.9%
2484
 
4.2%
2484
 
4.2%
2484
 
4.2%
2274
 
3.8%
1084
 
1.8%
Other values (20)10971
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter55220
92.4%
Other Punctuation4537
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11750
21.3%
11750
21.3%
6387
11.6%
3552
 
6.4%
2484
 
4.5%
2484
 
4.5%
2484
 
4.5%
2274
 
4.1%
1084
 
2.0%
992
 
1.8%
Other values (19)9979
18.1%
Other Punctuation
ValueCountFrequency (%)
,4537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul55220
92.4%
Common4537
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11750
21.3%
11750
21.3%
6387
11.6%
3552
 
6.4%
2484
 
4.5%
2484
 
4.5%
2484
 
4.5%
2274
 
4.1%
1084
 
2.0%
992
 
1.8%
Other values (19)9979
18.1%
Common
ValueCountFrequency (%)
,4537
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul55220
92.4%
ASCII4537
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
11750
21.3%
11750
21.3%
6387
11.6%
3552
 
6.4%
2484
 
4.5%
2484
 
4.5%
2484
 
4.5%
2274
 
4.1%
1084
 
2.0%
992
 
1.8%
Other values (19)9979
18.1%
ASCII
ValueCountFrequency (%)
,4537
100.0%

해당년도
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
2021년
15224 
2020년
6832 
2022년
 
800

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters114280
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022년
2nd row2022년
3rd row2022년
4th row2022년
5th row2022년

Common Values

ValueCountFrequency (%)
2021년15224
66.6%
2020년6832
29.9%
2022년800
 
3.5%

Length

2022-03-17T09:37:36.761004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:36.807847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2021년15224
66.6%
2020년6832
29.9%
2022년800
 
3.5%

Most occurring characters

ValueCountFrequency (%)
246512
40.7%
029688
26.0%
22856
20.0%
115224
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number91424
80.0%
Other Letter22856
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
246512
50.9%
029688
32.5%
115224
 
16.7%
Other Letter
ValueCountFrequency (%)
22856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common91424
80.0%
Hangul22856
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246512
50.9%
029688
32.5%
115224
 
16.7%
Hangul
ValueCountFrequency (%)
22856
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII91424
80.0%
Hangul22856
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246512
50.9%
029688
32.5%
115224
 
16.7%
Hangul
ValueCountFrequency (%)
22856
100.0%

거주자금액나눔
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
90,000 ~ 110,000원
6791 
90,000원 이하
4124 
140,000 ~ 160,000원
3356 
130,000 ~ 140,000원
2267 
110,000 ~ 120,000원
2101 
Other values (4)
4217 

Length

Max length18
Median length18
Mean length16.195091
Min length10

Characters and Unicode

Total characters370155
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row140,000 ~ 160,000원
2nd row140,000 ~ 160,000원
3rd row130,000 ~ 140,000원
4th row140,000 ~ 160,000원
5th row130,000 ~ 140,000원

Common Values

ValueCountFrequency (%)
90,000 ~ 110,000원6791
29.7%
90,000원 이하4124
18.0%
140,000 ~ 160,000원3356
14.7%
130,000 ~ 140,000원2267
 
9.9%
110,000 ~ 120,000원2101
 
9.2%
120,000 ~ 130,000원1876
 
8.2%
160,000 ~ 180,000원1689
 
7.4%
180,000 ~ 200,000원442
 
1.9%
200,000원 이상210
 
0.9%

Length

2022-03-17T09:37:36.874583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:36.942441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
18522
28.8%
90,0006791
 
10.6%
110,000원6791
 
10.6%
90,000원4124
 
6.4%
이하4124
 
6.4%
140,0003356
 
5.2%
160,000원3356
 
5.2%
130,0002267
 
3.5%
140,000원2267
 
3.5%
120,000원2101
 
3.3%
Other values (8)10535
16.4%

Most occurring characters

ValueCountFrequency (%)
0166164
44.9%
,41378
 
11.2%
41378
 
11.2%
138703
 
10.5%
22856
 
6.2%
~18522
 
5.0%
910915
 
2.9%
45623
 
1.5%
65045
 
1.4%
24629
 
1.3%
Other values (5)14942
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number237353
64.1%
Other Punctuation41378
 
11.2%
Space Separator41378
 
11.2%
Other Letter31524
 
8.5%
Math Symbol18522
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0166164
70.0%
138703
 
16.3%
910915
 
4.6%
45623
 
2.4%
65045
 
2.1%
24629
 
2.0%
34143
 
1.7%
82131
 
0.9%
Other Letter
ValueCountFrequency (%)
22856
72.5%
4334
 
13.7%
4124
 
13.1%
210
 
0.7%
Other Punctuation
ValueCountFrequency (%)
,41378
100.0%
Space Separator
ValueCountFrequency (%)
41378
100.0%
Math Symbol
ValueCountFrequency (%)
~18522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common338631
91.5%
Hangul31524
 
8.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0166164
49.1%
,41378
 
12.2%
41378
 
12.2%
138703
 
11.4%
~18522
 
5.5%
910915
 
3.2%
45623
 
1.7%
65045
 
1.5%
24629
 
1.4%
34143
 
1.2%
Hangul
ValueCountFrequency (%)
22856
72.5%
4334
 
13.7%
4124
 
13.1%
210
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII338631
91.5%
Hangul31524
 
8.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0166164
49.1%
,41378
 
12.2%
41378
 
12.2%
138703
 
11.4%
~18522
 
5.5%
910915
 
3.2%
45623
 
1.7%
65045
 
1.5%
24629
 
1.4%
34143
 
1.2%
Hangul
ValueCountFrequency (%)
22856
72.5%
4334
 
13.7%
4124
 
13.1%
210
 
0.7%

비거주자나눔
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
90,000원 이하
6211 
180,000 ~ 200,000원
4393 
200,000원 이상
4261 
140,000 ~ 160,000원
2720 
160,000 ~ 180,000원
2064 
Other values (4)
3207 

Length

Max length18
Median length17
Mean length14.45817291
Min length10

Characters and Unicode

Total characters330456
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200,000원 이상
2nd row180,000 ~ 200,000원
3rd row130,000 ~ 140,000원
4th row180,000 ~ 200,000원
5th row130,000 ~ 140,000원

Common Values

ValueCountFrequency (%)
90,000원 이하6211
27.2%
180,000 ~ 200,000원4393
19.2%
200,000원 이상4261
18.6%
140,000 ~ 160,000원2720
11.9%
160,000 ~ 180,000원2064
 
9.0%
90,000 ~ 110,000원1437
 
6.3%
130,000 ~ 140,000원878
 
3.8%
120,000 ~ 130,000원471
 
2.1%
110,000 ~ 120,000원421
 
1.8%

Length

2022-03-17T09:37:37.036192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:37.103877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
12384
21.3%
200,000원8654
14.9%
90,000원6211
10.7%
이하6211
10.7%
180,0004393
 
7.6%
이상4261
 
7.3%
140,0002720
 
4.7%
160,000원2720
 
4.7%
180,000원2064
 
3.6%
160,0002064
 
3.6%
Other values (8)6414
11.0%

Most occurring characters

ValueCountFrequency (%)
0149614
45.3%
,35240
 
10.7%
35240
 
10.7%
22856
 
6.9%
120796
 
6.3%
~12384
 
3.7%
10472
 
3.2%
29546
 
2.9%
97648
 
2.3%
86457
 
2.0%
Other values (5)20203
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number203792
61.7%
Other Letter43800
 
13.3%
Other Punctuation35240
 
10.7%
Space Separator35240
 
10.7%
Math Symbol12384
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0149614
73.4%
120796
 
10.2%
29546
 
4.7%
97648
 
3.8%
86457
 
3.2%
64784
 
2.3%
43598
 
1.8%
31349
 
0.7%
Other Letter
ValueCountFrequency (%)
22856
52.2%
10472
23.9%
6211
 
14.2%
4261
 
9.7%
Other Punctuation
ValueCountFrequency (%)
,35240
100.0%
Space Separator
ValueCountFrequency (%)
35240
100.0%
Math Symbol
ValueCountFrequency (%)
~12384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common286656
86.7%
Hangul43800
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0149614
52.2%
,35240
 
12.3%
35240
 
12.3%
120796
 
7.3%
~12384
 
4.3%
29546
 
3.3%
97648
 
2.7%
86457
 
2.3%
64784
 
1.7%
43598
 
1.3%
Hangul
ValueCountFrequency (%)
22856
52.2%
10472
23.9%
6211
 
14.2%
4261
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII286656
86.7%
Hangul43800
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0149614
52.2%
,35240
 
12.3%
35240
 
12.3%
120796
 
7.3%
~12384
 
4.3%
29546
 
3.3%
97648
 
2.7%
86457
 
2.3%
64784
 
1.7%
43598
 
1.3%
Hangul
ValueCountFrequency (%)
22856
52.2%
10472
23.9%
6211
 
14.2%
4261
 
9.7%

예약년월일
Categorical

HIGH CARDINALITY

Distinct592
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
2021-07-21
 
172
2021-09-24
 
155
2021-09-23
 
146
2021-08-31
 
140
2021-09-17
 
136
Other values (587)
22107 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters228560
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row2022-03-01
2nd row2022-03-01
3rd row2022-03-01
4th row2022-03-01
5th row2022-03-01

Common Values

ValueCountFrequency (%)
2021-07-21172
 
0.8%
2021-09-24155
 
0.7%
2021-09-23146
 
0.6%
2021-08-31140
 
0.6%
2021-09-17136
 
0.6%
2021-09-14133
 
0.6%
2021-09-02126
 
0.6%
2021-04-06124
 
0.5%
2021-09-03124
 
0.5%
2021-08-02123
 
0.5%
Other values (582)21477
94.0%

Length

2022-03-17T09:37:37.196566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-07-21172
 
0.8%
2021-09-24155
 
0.7%
2021-09-23146
 
0.6%
2021-08-31140
 
0.6%
2021-09-17136
 
0.6%
2021-09-14133
 
0.6%
2021-09-02126
 
0.6%
2021-04-06124
 
0.5%
2021-09-03124
 
0.5%
2021-08-03123
 
0.5%
Other values (582)21477
94.0%

Most occurring characters

ValueCountFrequency (%)
061212
26.8%
257626
25.2%
-45712
20.0%
128366
12.4%
96156
 
2.7%
76020
 
2.6%
85632
 
2.5%
65184
 
2.3%
35092
 
2.2%
43831
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182848
80.0%
Dash Punctuation45712
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
061212
33.5%
257626
31.5%
128366
15.5%
96156
 
3.4%
76020
 
3.3%
85632
 
3.1%
65184
 
2.8%
35092
 
2.8%
43831
 
2.1%
53729
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-45712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common228560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
061212
26.8%
257626
25.2%
-45712
20.0%
128366
12.4%
96156
 
2.7%
76020
 
2.6%
85632
 
2.5%
65184
 
2.3%
35092
 
2.2%
43831
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII228560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
061212
26.8%
257626
25.2%
-45712
20.0%
128366
12.4%
96156
 
2.7%
76020
 
2.6%
85632
 
2.5%
65184
 
2.3%
35092
 
2.2%
43831
 
1.7%

사용년월일
Categorical

HIGH CARDINALITY

Distinct633
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
2021-08-16
 
198
2021-09-22
 
146
2021-09-26
 
144
2021-06-30
 
138
2021-09-24
 
137
Other values (628)
22093 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters228560
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row2022-03-09
2nd row2022-03-15
3rd row2022-03-14
4th row2022-03-15
5th row2022-03-14

Common Values

ValueCountFrequency (%)
2021-08-16198
 
0.9%
2021-09-22146
 
0.6%
2021-09-26144
 
0.6%
2021-06-30138
 
0.6%
2021-09-24137
 
0.6%
2021-07-30132
 
0.6%
2021-09-25130
 
0.6%
2021-10-11127
 
0.6%
2021-09-23121
 
0.5%
2021-09-27118
 
0.5%
Other values (623)21465
93.9%

Length

2022-03-17T09:37:37.268327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-08-16198
 
0.9%
2021-09-22146
 
0.6%
2021-09-26144
 
0.6%
2021-06-30138
 
0.6%
2021-09-24137
 
0.6%
2021-07-30132
 
0.6%
2021-09-25130
 
0.6%
2021-10-11127
 
0.6%
2021-09-23121
 
0.5%
2021-09-18118
 
0.5%
Other values (623)21465
93.9%

Most occurring characters

ValueCountFrequency (%)
059866
26.2%
257517
25.2%
-45712
20.0%
130849
13.5%
96052
 
2.6%
75678
 
2.5%
85635
 
2.5%
35403
 
2.4%
64579
 
2.0%
53737
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182848
80.0%
Dash Punctuation45712
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
059866
32.7%
257517
31.5%
130849
16.9%
96052
 
3.3%
75678
 
3.1%
85635
 
3.1%
35403
 
3.0%
64579
 
2.5%
53737
 
2.0%
43532
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
-45712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common228560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
059866
26.2%
257517
25.2%
-45712
20.0%
130849
13.5%
96052
 
2.6%
75678
 
2.5%
85635
 
2.5%
35403
 
2.4%
64579
 
2.0%
53737
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII228560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
059866
26.2%
257517
25.2%
-45712
20.0%
130849
13.5%
96052
 
2.6%
75678
 
2.5%
85635
 
2.5%
35403
 
2.4%
64579
 
2.0%
53737
 
1.6%

날짜차이
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.64044452
Minimum0
Maximum85
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:37.354039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q315
95-th percentile33
Maximum85
Range85
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.96132932
Coefficient of variation (CV)0.9416589975
Kurtosis5.157914438
Mean11.64044452
Median Absolute Deviation (MAD)5
Skewness2.007915631
Sum266054
Variance120.1507405
MonotonicityNot monotonic
2022-03-17T09:37:37.450715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11864
 
8.2%
61473
 
6.4%
31462
 
6.4%
21438
 
6.3%
41434
 
6.3%
51360
 
6.0%
71332
 
5.8%
81169
 
5.1%
91067
 
4.7%
10961
 
4.2%
Other values (71)9296
40.7%
ValueCountFrequency (%)
012
 
0.1%
11864
8.2%
21438
6.3%
31462
6.4%
41434
6.3%
51360
6.0%
61473
6.4%
71332
5.8%
81169
5.1%
91067
4.7%
ValueCountFrequency (%)
851
< 0.1%
841
< 0.1%
801
< 0.1%
771
< 0.1%
762
< 0.1%
751
< 0.1%
742
< 0.1%
732
< 0.1%
722
< 0.1%
712
< 0.1%

날짜차이요일
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
3 ~ 7일전
7061 
8 ~ 15일
7059 
16일 ~
5422 
1 ~ 2일전
3302 
당일예약
 
12

Length

Max length7
Median length7
Mean length6.523976199
Min length4

Characters and Unicode

Total characters149112
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8 ~ 15일
2nd row8 ~ 15일
3rd row8 ~ 15일
4th row8 ~ 15일
5th row8 ~ 15일

Common Values

ValueCountFrequency (%)
3 ~ 7일전7061
30.9%
8 ~ 15일7059
30.9%
16일 ~5422
23.7%
1 ~ 2일전3302
14.4%
당일예약12
 
0.1%

Length

2022-03-17T09:37:37.539506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:37.596321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
22844
36.2%
37061
 
11.2%
7일전7061
 
11.2%
87059
 
11.2%
15일7059
 
11.2%
16일5422
 
8.6%
13302
 
5.2%
2일전3302
 
5.2%
당일예약12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
40266
27.0%
22856
15.3%
~22844
15.3%
115783
 
10.6%
10363
 
6.9%
37061
 
4.7%
77061
 
4.7%
87059
 
4.7%
57059
 
4.7%
65422
 
3.6%
Other values (4)3338
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number52747
35.4%
Space Separator40266
27.0%
Other Letter33255
22.3%
Math Symbol22844
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115783
29.9%
37061
13.4%
77061
13.4%
87059
13.4%
57059
13.4%
65422
 
10.3%
23302
 
6.3%
Other Letter
ValueCountFrequency (%)
22856
68.7%
10363
31.2%
12
 
< 0.1%
12
 
< 0.1%
12
 
< 0.1%
Space Separator
ValueCountFrequency (%)
40266
100.0%
Math Symbol
ValueCountFrequency (%)
~22844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common115857
77.7%
Hangul33255
 
22.3%

Most frequent character per script

Common
ValueCountFrequency (%)
40266
34.8%
~22844
19.7%
115783
 
13.6%
37061
 
6.1%
77061
 
6.1%
87059
 
6.1%
57059
 
6.1%
65422
 
4.7%
23302
 
2.9%
Hangul
ValueCountFrequency (%)
22856
68.7%
10363
31.2%
12
 
< 0.1%
12
 
< 0.1%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII115857
77.7%
Hangul33255
 
22.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40266
34.8%
~22844
19.7%
115783
 
13.6%
37061
 
6.1%
77061
 
6.1%
87059
 
6.1%
57059
 
6.1%
65422
 
4.7%
23302
 
2.9%
Hangul
ValueCountFrequency (%)
22856
68.7%
10363
31.2%
12
 
< 0.1%
12
 
< 0.1%
12
 
< 0.1%

예약시간(시)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.29497725
Minimum0
Maximum45
Zeros231
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size178.7 KiB
2022-03-17T09:37:37.666069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q111
median14
Q318
95-th percentile21
Maximum45
Range45
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.520466903
Coefficient of variation (CV)0.3162276388
Kurtosis0.2785970045
Mean14.29497725
Median Absolute Deviation (MAD)3
Skewness-0.3279447803
Sum326726
Variance20.43462102
MonotonicityNot monotonic
2022-03-17T09:37:37.744814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
131810
 
7.9%
161793
 
7.8%
111785
 
7.8%
151780
 
7.8%
141771
 
7.7%
171729
 
7.6%
101684
 
7.4%
121679
 
7.3%
181458
 
6.4%
91269
 
5.6%
Other values (17)6098
26.7%
ValueCountFrequency (%)
0231
 
1.0%
1120
 
0.5%
271
 
0.3%
345
 
0.2%
432
 
0.1%
575
 
0.3%
6214
 
0.9%
7322
 
1.4%
8605
2.6%
91269
5.6%
ValueCountFrequency (%)
451
 
< 0.1%
282
 
< 0.1%
251
 
< 0.1%
23444
 
1.9%
22681
 
3.0%
21898
3.9%
201113
4.9%
191243
5.4%
181458
6.4%
171729
7.6%

예약시간대별
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size178.7 KiB
12시 ~ 18시
10562 
18시 ~ 24시
6068 
07시 ~ 12시
5665 
01시 ~ 07시
 
557

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters205668
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07시 ~ 12시
2nd row07시 ~ 12시
3rd row07시 ~ 12시
4th row07시 ~ 12시
5th row07시 ~ 12시

Common Values

ValueCountFrequency (%)
12시 ~ 18시10562
46.2%
18시 ~ 24시6068
26.5%
07시 ~ 12시5665
24.8%
01시 ~ 07시557
 
2.4%
(Missing)4
 
< 0.1%

Length

2022-03-17T09:37:37.822476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:37.875824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
22852
33.3%
18시16630
24.3%
12시16227
23.7%
07시6222
 
9.1%
24시6068
 
8.9%
01시557
 
0.8%

Most occurring characters

ValueCountFrequency (%)
45704
22.2%
45704
22.2%
133414
16.2%
~22852
11.1%
222295
10.8%
816630
 
8.1%
06779
 
3.3%
76222
 
3.0%
46068
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number91408
44.4%
Other Letter45704
22.2%
Space Separator45704
22.2%
Math Symbol22852
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
133414
36.6%
222295
24.4%
816630
18.2%
06779
 
7.4%
76222
 
6.8%
46068
 
6.6%
Other Letter
ValueCountFrequency (%)
45704
100.0%
Space Separator
ValueCountFrequency (%)
45704
100.0%
Math Symbol
ValueCountFrequency (%)
~22852
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159964
77.8%
Hangul45704
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
45704
28.6%
133414
20.9%
~22852
14.3%
222295
13.9%
816630
 
10.4%
06779
 
4.2%
76222
 
3.9%
46068
 
3.8%
Hangul
ValueCountFrequency (%)
45704
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII159964
77.8%
Hangul45704
 
22.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
45704
100.0%
ASCII
ValueCountFrequency (%)
45704
28.6%
133414
20.9%
~22852
14.3%
222295
13.9%
816630
 
10.4%
06779
 
4.2%
76222
 
3.9%
46068
 
3.8%

사용일통계
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.7 KiB
12시 ~
8418 
06시 ~ 08시
8226 
08시 ~ 10시
4567 
10시 ~ 12시
1645 

Length

Max length9
Median length9
Mean length7.526776339
Min length5

Characters and Unicode

Total characters172032
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08시 ~ 10시
2nd row12시 ~
3rd row06시 ~ 08시
4th row12시 ~
5th row06시 ~ 08시

Common Values

ValueCountFrequency (%)
12시 ~8418
36.8%
06시 ~ 08시8226
36.0%
08시 ~ 10시4567
20.0%
10시 ~ 12시1645
 
7.2%

Length

2022-03-17T09:37:37.944618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-17T09:37:38.002460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
22856
38.0%
08시12793
21.3%
12시10063
16.7%
06시8226
 
13.7%
10시6212
 
10.3%

Most occurring characters

ValueCountFrequency (%)
37294
21.7%
37294
21.7%
027231
15.8%
~22856
13.3%
116275
9.5%
812793
 
7.4%
210063
 
5.8%
68226
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number74588
43.4%
Other Letter37294
21.7%
Space Separator37294
21.7%
Math Symbol22856
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027231
36.5%
116275
21.8%
812793
17.2%
210063
 
13.5%
68226
 
11.0%
Other Letter
ValueCountFrequency (%)
37294
100.0%
Space Separator
ValueCountFrequency (%)
37294
100.0%
Math Symbol
ValueCountFrequency (%)
~22856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common134738
78.3%
Hangul37294
 
21.7%

Most frequent character per script

Common
ValueCountFrequency (%)
37294
27.7%
027231
20.2%
~22856
17.0%
116275
12.1%
812793
 
9.5%
210063
 
7.5%
68226
 
6.1%
Hangul
ValueCountFrequency (%)
37294
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII134738
78.3%
Hangul37294
 
21.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
37294
100.0%
ASCII
ValueCountFrequency (%)
37294
27.7%
027231
20.2%
~22856
17.0%
116275
12.1%
812793
 
9.5%
210063
 
7.5%
68226
 
6.1%

Interactions

2022-03-17T09:37:30.656305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.263502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.342159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.305637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.232166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.379007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.396491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.373486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.513034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.771016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.373433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.456783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.424515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.346866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.492426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.506168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.502058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.623583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.896502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.478239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.556464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.522251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.458578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.595155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.618747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.645576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.722359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.017419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.579889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.659120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.614936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.556216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.701923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.742597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.774144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.862781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.127176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.690849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.774241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.718130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.660936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.817749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.866190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.899727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.116157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.274674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:22.938601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.894909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.832706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.809460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.951486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.977809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.036456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.255644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.369749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.035511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.992512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.929888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.915157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.071090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.072492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.177073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.342450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.519250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.138271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.099155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.036555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.019201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.184922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.175236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.283717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.444101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:31.644828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:23.239606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:24.203804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:25.132326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:26.124920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:27.288571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:28.277806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:29.400327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-17T09:37:30.563615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-17T09:37:38.209800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-17T09:37:38.398173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-17T09:37:38.559650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-17T09:37:38.732004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-17T09:37:38.932382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-17T09:37:31.938420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-17T09:37:32.643241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-17T09:37:32.939333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-17T09:37:33.103700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

골프장명골프장지역명골프장멤버십값골프장유형값사용일자사용시간값사용유형명거주자금액비거주자금액결제수단명총인원수거주자인원수비거주자인원수예약일자예약시간판매업체명예약자성별코드예약자연령대코드예약자거주지값예약자평균값예약자골프경력값예약자평균사용값예약자사용성향값해당년도거주자금액나눔비거주자나눔예약년월일사용년월일날짜차이날짜차이요일예약시간(시)예약시간대별사용일통계
0아덴힐GC서부권대중제1820220309852새별155000220000현장결제4402022030181639아덴힐GC150.0도민1210년이상5회이상주말골프2022년140,000 ~ 160,000원200,000원 이상2022-03-012022-03-0988 ~ 15일807시 ~ 12시08시 ~ 10시
1라온GC서부권대중제27202203151235LAKE152500182500현장결제4402022030181741라온GC1NaN도민2010년이상5회이상주중골프,오후골프2022년140,000 ~ 160,000원180,000 ~ 200,000원2022-03-012022-03-15148 ~ 15일807시 ~ 12시12시 ~
2그린필드CC제주시내권회원제1820220314756M132000132000선결제44020220301100047(주)부민가자투어1NaN도민20NaNNaNNaN2022년130,000 ~ 140,000원130,000 ~ 140,000원2022-03-012022-03-14138 ~ 15일1007시 ~ 12시06시 ~ 08시
3라온GC서부권대중제27202203151228LAKE152500182500현장결제4402022030182312라온GC160.0도민1410년이상3회주중골프,오후골프2022년140,000 ~ 160,000원180,000 ~ 200,000원2022-03-012022-03-15148 ~ 15일807시 ~ 12시12시 ~
4그린필드CC제주시내권회원제1820220314749M132000132000선결제44020220301100053(주)부민가자투어240.0도민25NaNNaNNaN2022년130,000 ~ 140,000원130,000 ~ 140,000원2022-03-012022-03-14138 ~ 15일1007시 ~ 12시06시 ~ 08시
5아덴힐GC서부권대중제18202203081135왕이메130000190000현장결제44020220301103335아덴힐GC140.0도민1510년이상4회정기골프2022년130,000 ~ 140,000원180,000 ~ 200,000원2022-03-012022-03-0873 ~ 7일전1007시 ~ 12시10시 ~ 12시
6아덴힐GC서부권대중제18202203041231새별130000190000현장결제44020220301105715아덴힐GC150.0도민10NaNNaNNaN2022년130,000 ~ 140,000원180,000 ~ 200,000원2022-03-012022-03-0433 ~ 7일전1007시 ~ 12시12시 ~
7아덴힐GC서부권대중제18202203101313왕이메130000190000현장결제44020220301114220아덴힐GC130.0도민321년미만3회골프초년생2022년130,000 ~ 140,000원180,000 ~ 200,000원2022-03-012022-03-1098 ~ 15일1107시 ~ 12시12시 ~
8아덴힐GC서부권대중제18202203081128왕이메130000190000현장결제44020220301114628아덴힐GC240.0도민204~6년4회골프초년생2022년130,000 ~ 140,000원180,000 ~ 200,000원2022-03-012022-03-0873 ~ 7일전1107시 ~ 12시10시 ~ 12시
9아덴힐GC서부권대중제1820220310817새별130000190000현장결제44020220301115016아덴힐GC240.0도민NaNNaNNaNNaN2022년130,000 ~ 140,000원180,000 ~ 200,000원2022-03-012022-03-1098 ~ 15일1107시 ~ 12시08시 ~ 10시

Last rows

골프장명골프장지역명골프장멤버십값골프장유형값사용일자사용시간값사용유형명거주자금액비거주자금액결제수단명총인원수거주자인원수비거주자인원수예약일자예약시간판매업체명예약자성별코드예약자연령대코드예약자거주지값예약자평균값예약자골프경력값예약자평균사용값예약자사용성향값해당년도거주자금액나눔비거주자나눔예약년월일사용년월일날짜차이날짜차이요일예약시간(시)예약시간대별사용일통계
22846해비치CC동부권회원제+대중제36202110311241VALLEY170000230000현장결제44020211021125358해비치CC130.0도민7NaNNaNNaN2021년160,000 ~ 180,000원200,000원 이상2021-10-212021-10-31108 ~ 15일1212시 ~ 18시12시 ~
22847해비치CC동부권회원제+대중제36202110301159VALLEY170000230000현장결제44020211021155644해비치CC140.0도민NaN1~3년1회이하골프초년생2021년160,000 ~ 180,000원200,000원 이상2021-10-212021-10-3098 ~ 15일1512시 ~ 18시10시 ~ 12시
22848제주CC제주시권대중제1820211101724랜덤135000135000선결제44020211021162429슬기로운골프생활130.0도민NaN1~3년1회이하골프초년생,주중골프2021년130,000 ~ 140,000원130,000 ~ 140,000원2021-10-212021-11-01118 ~ 15일1612시 ~ 18시06시 ~ 08시
22849해비치CC동부권회원제+대중제3620211025726PALM160000220000현장결제44020211021171309해비치CC140.0도민154~6년4회내기파,번개골프2021년160,000 ~ 180,000원200,000원 이상2021-10-212021-10-2543 ~ 7일전1712시 ~ 18시06시 ~ 08시
22850라온GC서부권대중제27202110241303STONE192500222500현장결제4222021101985302라온GC130.0도민18NaNNaNNaN2021년180,000 ~ 200,000원200,000원 이상2021-10-192021-10-2453 ~ 7일전807시 ~ 12시12시 ~
22851제주CC제주시권대중제18202110301233랜덤165000165000선결제44020211024144906슬기로운골프생활1NaN도민NaNNaNNaNNaN2021년160,000 ~ 180,000원160,000 ~ 180,000원2021-10-242021-10-3063 ~ 7일전1412시 ~ 18시12시 ~
22852제주CC제주시권대중제1820211102717랜덤135000135000선결제44020211024144915슬기로운골프생활250.0도민244~6년2회새벽골프2021년130,000 ~ 140,000원130,000 ~ 140,000원2021-10-242021-11-0298 ~ 15일1412시 ~ 18시06시 ~ 08시
22853해비치CC동부권회원제+대중제36202110301248PALM200000260000현장결제44020211028133253해비치CC0NaN도민NaN1년미만4회골프초년생,주중골프,음주가무2021년200,000원 이상200,000원 이상2021-10-282021-10-3021 ~ 2일전1312시 ~ 18시12시 ~
22854해비치CC동부권회원제+대중제3620211101712LAKE150000190000현장결제44020211029163936해비치CC150.0도민18NaNNaNNaN2021년140,000 ~ 160,000원180,000 ~ 200,000원2021-10-292021-11-0133 ~ 7일전1612시 ~ 18시06시 ~ 08시
22855해비치CC동부권회원제+대중제3620211106712LAKE190000230000현장결제43120211029164201해비치CC130.0도민NaN4~6년4회골프초년생2021년180,000 ~ 200,000원200,000원 이상2021-10-292021-11-0688 ~ 15일1612시 ~ 18시06시 ~ 08시

Duplicate rows

Most frequently occurring

골프장명골프장지역명골프장멤버십값골프장유형값사용일자사용시간값사용유형명거주자금액비거주자금액결제수단명총인원수거주자인원수비거주자인원수예약일자예약시간판매업체명예약자성별코드예약자연령대코드예약자거주지값예약자평균값예약자골프경력값예약자평균사용값예약자사용성향값해당년도거주자금액나눔비거주자나눔예약년월일사용년월일날짜차이날짜차이요일예약시간(시)예약시간대별사용일통계# duplicates
0더클래식CC동부권대중제1820210707705대중제(Valley)7000090000현장결제44020210617115910섬프로150.0도민1210년이상4회주말골프2021년90,000원 이하90,000 ~ 110,000원2021-06-172021-07-072016일 ~1107시 ~ 12시06시 ~ 08시2